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1,144 claims from 719 papers are on the record. 42 have been checked so far; the other 1,102 have no check with a result yet.

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Status: Unchecked ×Topic: Topic Modeling ×Clear all

53 claims from 31 papers, showing 21–31 of 31

1. Computer Science › Topic Modeling

   Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models

   Wang, Xu, Lan et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“The experimental results over GPT-3 show that our proposed zero-shot prompting consistently outperforms Zero-shot-CoT across all datasets by a large margin, is comparable to or exceeds Zero-shot-Program-of-Thought Prompting, and has comparable performance wi…

2. Computer Science › Topic Modeling

   RWKV: Reinventing RNNs for the Transformer Era

   Peng, Alcaide, Anthony et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Our approach leverages a linear attention mechanism and allows us to formulate the model as either a Transformer or an RNN, thus parallelizing computations during training and maintains constant computational and memory complexity during inference.”

3. Computer Science › Topic Modeling

   Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

   Hsieh, Li, Yeh et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes.”

4. Computer Science › Topic Modeling

   Text Classification via Large Language Models

   Sun, Li, Li et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“Remarkably, CARP yields new SOTA performances on 4 out of 5 widely-used text-classification benchmarks, 97.39 (+1.24) on SST-2, 96.40 (+0.72) on AGNews, 98.78 (+0.25) on R8 and 96.95 (+0.6) on R52, and a performance comparable to SOTA on MR (92.39 v.s. 93.3)…

5. Computer Science › Topic Modeling

   LinkBERT: Pretraining Language Models with Document Links

   Yasunaga, Leskovec and Liang · arXiv (Cornell University) · 2022

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   1. Unchecked“We show that LinkBERT outperforms BERT on various downstream tasks across two domains: the general domain (pretrained on Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with citation links).”
   2. Unchecked“LinkBERT is especially effective for multi-hop reasoning and few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA), and our biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7% on BioASQ and USMLE).”

6. Computer Science › Topic Modeling

   TheoremQA: A Theorem-driven Question Answering dataset

   Chen, Yin, Ku et al. · arXiv (Cornell University) · 2023

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   1. Unchecked“We found that GPT-4's capabilities to solve these problems are unparalleled, achieving an accuracy of 51% with Program-of-Thoughts Prompting.”
   2. Unchecked“All the existing open-sourced models are below 15%, barely surpassing the random-guess baseline.”

7. Computer Science › Topic Modeling

   On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning

   Shaikh, Zhang, William, Bernstein and Yang · arXiv (Cornell University) · 2022

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   1. Unchecked“We find that zero-shot CoT reasoning in sensitive domains significantly increases a model's likelihood to produce harmful or undesirable output, with trends holding across different prompt formats and model variants.”
   2. Unchecked“Furthermore, we show that harmful CoTs increase with model size, but decrease with improved instruction following.”

8. Computer Science › Topic Modeling

   Language Model Behavior: A Comprehensive Survey

   Chang and Bergen · arXiv (Cornell University) · 2023

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   1. Unchecked“Language models possess basic capabilities in syntax, semantics, pragmatics, world knowledge, and reasoning, but these capabilities are sensitive to specific inputs and surface features.”
   2. Unchecked“Many of these weaknesses can be framed as over-generalizations or under-generalizations of learned patterns in text.”

9. Computer Science › Topic Modeling

   On the Role of Bidirectionality in Language Model Pre-Training

   Artetxe, Du, Goyal, Zettlemoyer and Stoyanov · arXiv (Cornell University) · 2022

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   1. Unchecked“We find that the optimal configuration is largely application-dependent (e.g., bidirectional attention is beneficial for fine-tuning and infilling, but harmful for next token prediction and zero-shot priming).”
   2. Unchecked“We train models with up to 6.7B parameters, and find differences to remain consistent at scale.”

10. Computer Science › Topic Modeling

    Inverse scaling can become U-shaped

    Jason, Najoung, Tay and Le · arXiv (Cornell University) · 2022

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    1. Unchecked“With this increased range of model sizes and training compute, only four out of the eleven tasks remain inverse scaling.”
    2. Unchecked“In addition, we find that 1-shot examples and chain-of-thought can help mitigate undesirable scaling patterns even further.”
    3. Unchecked“Six out of the eleven tasks exhibit "U-shaped scaling", where performance decreases up to a certain size, and then increases again up to the largest model evaluated (the one remaining task displays positive scaling).”

11. Computer Science › Topic Modeling

    Scaling Language Models: Methods, Analysis & Insights from Training Gopher

    Rae, Borgeaud, Cai et al. · arXiv (Cornell University) · 2021

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    1. Unchecked“Gains from scale are largest in areas such as reading comprehension, fact-checking, and the identification of toxic language, but logical and mathematical reasoning see less benefit.”
    2. Unchecked“These models are evaluated on 152 diverse tasks, achieving state-of-the-art performance across the majority.”